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Bayesian assessment of Lorenz and stochastic dominance
Author(s) -
Lander David,
Gunawan David,
Griffiths William,
Chotikapanich Duangkamon
Publication year - 2020
Publication title -
canadian journal of economics/revue canadienne d'économique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 69
eISSN - 1540-5982
pISSN - 0008-4085
DOI - 10.1111/caje.12443
Subject(s) - stochastic dominance , dominance (genetics) , markov chain monte carlo , lorenz curve , bayesian probability , mathematics , econometrics , statistics , markov chain , statistical physics , population , inequality , physics , demography , sociology , mathematical analysis , biology , economic inequality , gini coefficient , biochemistry , gene
We introduce a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y , estimated via Markov chain Monte Carlo, we describe how to compute posterior probabilities for: (i) X dominates Y , (ii) Y dominates X and (iii) neither Y nor X dominates. The proposed approach is applied to Indonesian income distributions using mixtures of gamma densities that ensure flexible modelling. Probability curves depicting the probability of dominance at each population proportion are used to explain changes in dominance probabilities over restricted ranges relevant for poverty orderings. They also explain some seemingly contradictory outcomes from the p ‐values of some sampling theory tests.